A genetic algorithm with constrained sorting method for constrained optimization problems

Engineering problems are commonly optimization problems with various constraints. For solving these constrained optimization problems, an effective genetic algorithm with a constrained sorting method is proposed in this work. The constrained sorting method is based on a dynamic penalty function and a non-dominated sorting technique that is used for ranking all the feasible and infeasible solutions in the whole evolutionary population. The proposed algorithm is tested on five well-known benchmark functions and three engineering problems. Experimental results and comparisons with previously reported results demonstrate the effectiveness, efficiency and robustness of the present algorithm for constrained optimization problems.